R-universe search: topic:regression-models

ddsjoberg

gtsummary:Presentation-Ready Data Summary and Analytic Result Tables

Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.

Maintained by Daniel D. Sjoberg. Last updated 4 days ago.

easy-to-use gt html5 regression-models reproducibility reproducible-research statistics summary-statistics summary-tables table1 tableone

1.1k stars 17.02 score 8.2k scripts 15 dependents

easystats

parameters:Processing of Model Parameters

Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).

Maintained by Daniel Lüdecke. Last updated 8 days ago.

beta bootstrap ci confidence-intervals data-reduction easystats fa feature-extraction feature-reduction hacktoberfest parameters pca pvalues regression-models robust-statistics standardize standardized-estimates statistical-models

454 stars 15.67 score 1.8k scripts 56 dependents

indrajeetpatil

ggstatsplot:'ggplot2' Based Plots with Statistical Details

Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) <doi:10.21105/joss.03236>.

Maintained by Indrajeet Patil. Last updated 1 months ago.

bayes-factors datascience dataviz effect-size ggplot-extension hypothesis-testing non-parametric-statistics regression-models statistical-analysis

2.1k stars 14.46 score 3.0k scripts 1 dependents

easystats

easystats:Framework for Easy Statistical Modeling, Visualization, and Reporting

A meta-package that installs and loads a set of packages from 'easystats' ecosystem in a single step. This collection of packages provide a unifying and consistent framework for statistical modeling, visualization, and reporting. Additionally, it provides articles targeted at instructors for teaching 'easystats', and a dashboard targeted at new R users for easily conducting statistical analysis by accessing summary results, model fit indices, and visualizations with minimal programming.

Maintained by Daniel Lüdecke. Last updated 25 days ago.

dataanalytics datascience easystats hacktoberfest models performance-metrics regression-models statistics

1.1k stars 13.01 score 1.8k scripts 1 dependents

config-i1

greybox:Toolbox for Model Building and Forecasting

Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.

Maintained by Ivan Svetunkov. Last updated 15 days ago.

forecasting model-selection model-selection-and-evaluation regression regression-models statistics cpp

30 stars 11.03 score 97 scripts 34 dependents

fsolt

dotwhisker:Dot-and-Whisker Plots of Regression Results

Create quick and easy dot-and-whisker plots of regression results. It takes as input either (1) a coefficient table in standard form or (2) one (or a list of) fitted model objects (of any type that has methods implemented in the 'parameters' package). It returns 'ggplot' objects that can be further customized using tools from the 'ggplot2' package. The package also includes helper functions for tasks such as rescaling coefficients or relabeling predictor variables. See more methodological discussion of the visualization and data management methods used in this package in Kastellec and Leoni (2007) <doi:10.1017/S1537592707072209> and Gelman (2008) <doi:10.1002/sim.3107>.

Maintained by Yue Hu. Last updated 6 months ago.

graphics plot regression-models

60 stars 10.14 score 680 scripts

hongooi73

glmnetUtils:Utilities for 'Glmnet'

Provides a formula interface for the 'glmnet' package for elasticnet regression, a method for cross-validating the alpha parameter, and other quality-of-life tools.

Maintained by Hong Ooi. Last updated 2 years ago.

cross-validation glmnet lasso regression-models

65 stars 9.17 score 339 scripts 5 dependents

graemeleehickey

joineRML:Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

Maintained by Graeme L. Hickey. Last updated 2 months ago.

armadillo biostatistics clinical-trials cox dynamic joint-models longitudinal-data multivariate-analysis multivariate-data multivariate-longitudinal-data prediction rcpp regression-models statistics survival openblas cpp openmp

30 stars 8.93 score 146 scripts 1 dependents

modeloriented

auditor:Model Audit - Verification, Validation, and Error Analysis

Provides an easy to use unified interface for creating validation plots for any model. The 'auditor' helps to avoid repetitive work consisting of writing code needed to create residual plots. This visualizations allow to asses and compare the goodness of fit, performance, and similarity of models.

Maintained by Alicja Gosiewska. Last updated 1 years ago.

classification error-analysis explainable-artificial-intelligence machine-learning model-validation regression-models residuals xai

58 stars 8.76 score 94 scripts 2 dependents

brian-j-smith

MachineShop:Machine Learning Models and Tools

Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.

Maintained by Brian J Smith. Last updated 7 months ago.

classification-models machine-learning predictive-modeling regression-models survival-models

62 stars 7.95 score 121 scripts

mthulin

boot.pval:Bootstrap p-Values

Computation of bootstrap p-values through inversion of confidence intervals, including convenience functions for regression models and tests of location.

Maintained by Måns Thulin. Last updated 23 days ago.

bootstrap p-value regression-models

4 stars 7.89 score 36 scripts 3 dependents

sahirbhatnagar

casebase:Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression

Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.

Maintained by Sahir Bhatnagar. Last updated 7 months ago.

competing-risks cox-regression regression-models survival-analysis

9 stars 7.16 score 94 scripts

nelson-gon

manymodelr:Build and Tune Several Models

Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) <doi:10.48550/arXiv.1405.6974>.

Maintained by Nelson Gonzabato. Last updated 9 days ago.

analysis-of-variance anova correlation correlation-coefficient generalized-linear-models gradient-boosting-decision-trees knn-classification linear-models linear-regression machine-learning missing-values models r-programming random-forest-algorithm regression-models

2 stars 5.78 score 50 scripts

sestelo

npregfast:Nonparametric Estimation of Regression Models with Factor-by-Curve Interactions

A method for obtaining nonparametric estimates of regression models with or without factor-by-curve interactions using local polynomial kernel smoothers or splines. Additionally, a parametric model (allometric model) can be estimated.

Maintained by Marta Sestelo. Last updated 3 months ago.

allometric barnacle critical-points curve-interactions factor-by-curve fortran interaction nonparametric regression-models testing

5 stars 5.73 score 89 scripts 2 dependents

guyabel

tidycat:Expand Tidy Output for Categorical Parameter Estimates

Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates.

Maintained by Guy J. Abel. Last updated 1 years ago.

data-visualization data-viz glm model-comparison regression-analysis regression-models statistical-analysis statistical-modeling

4 stars 5.53 score 56 scripts 1 dependents

cmusso86

recalibratiNN:Quantile Recalibration for Regression Models

Enables the diagnostics and enhancement of regression model calibration.It offers both global and local visualization tools for calibration diagnostics and provides one recalibration method: Torres R, Nott DJ, Sisson SA, Rodrigues T, Reis JG, Rodrigues GS (2024) <doi:10.48550/arXiv.2403.05756>. The method leverages on Probabilistic Integral Transform (PIT) values to both evaluate and perform the calibration of statistical models. For a more detailed description of the package, please refer to the bachelor's thesis available bellow.

Maintained by Carolina Musso. Last updated 2 months ago.

calibration gaussian-models neural-network probability recalibration regression-models

7 stars 5.32 score 8 scripts

stopsack

risks:Estimate Risk Ratios and Risk Differences using Regression

Risk ratios and risk differences are estimated using regression models that allow for binary, categorical, and continuous exposures and confounders. Implemented are marginal standardization after fitting logistic models (g-computation) with delta-method and bootstrap standard errors, Miettinen's case-duplication approach (Schouten et al. 1993, <doi:10.1002/sim.4780121808>), log-binomial (Poisson) models with empirical variance (Zou 2004, <doi:10.1093/aje/kwh090>), binomial models with starting values from Poisson models (Spiegelman and Hertzmark 2005, <doi:10.1093/aje/kwi188>), and others.

Maintained by Konrad Stopsack. Last updated 9 days ago.

binomial biostatistics epidemiology regression-models

5 stars 5.26 score 12 scripts

gdkrmr

DRR:Dimensionality Reduction via Regression

An Implementation of Dimensionality Reduction via Regression using Kernel Ridge Regression.

Maintained by Guido Kraemer. Last updated 2 years ago.

dimensionality-reduction kernel-methods non-linear regression-models

9 stars 5.24 score 8 scripts 1 dependents

jobnmadu

Dyn4cast:Dynamic Modeling and Machine Learning Environment

Estimates, predict and forecast dynamic models as well as Machine Learning metrics which assists in model selection for further analysis. The package also have capabilities to provide tools and metrics that are useful in machine learning and modeling. For example, there is quick summary, percent sign, Mallow's Cp tools and others. The ecosystem of this package is analysis of economic data for national development. The package is so far stable and has high reliability and efficiency as well as time-saving.

Maintained by Job Nmadu. Last updated 13 days ago.

data-science equal-lenght-forecast forecasting knots machine-learning nigeria prediction regression-models spline-models statistics time-series

4 stars 5.03 score 38 scripts

friendly

genridge:Generalized Ridge Trace Plots for Ridge Regression

The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.

Maintained by Michael Friendly. Last updated 4 months ago.

bias-variance graphics principal-component-analysis regression-models ridge-regression singular-value-decomposition

4 stars 4.84 score 69 scripts

jared-fowler

prettyglm:Pretty Summaries of Generalized Linear Model Coefficients

One of the main advantages of using Generalised Linear Models is their interpretability. The goal of 'prettyglm' is to provide a set of functions which easily create beautiful coefficient summaries which can readily be shared and explained. 'prettyglm' helps users create coefficient summaries which include categorical base levels, variable importance and type III p.values. 'prettyglm' also creates beautiful relativity plots for categorical, continuous and splined coefficients.

Maintained by Jared Fowler. Last updated 1 years ago.

classification classification-model data-science data-visualization glm linear-models regression regression-analysis regression-model regression-models statistical-models

3 stars 4.73 score 36 scripts

sfcheung

lmhelprs:Helper Functions for Linear Model Analysis

A collection of helper functions for multiple regression models fitted by lm(). Most of them are simple functions for simple tasks which can be done with coding, but may not be easy for occasional users of R. Most of the tasks addressed are those sometimes needed when using the 'manymome' package (Cheung and Cheung, 2023, <doi:10.3758/s13428-023-02224-z>) and 'stdmod' package (Cheung, Cheung, Lau, Hui, and Vong, 2022, <doi:10.1037/hea0001188>). However, they can also be used in other scenarios.

Maintained by Shu Fai Cheung. Last updated 1 months ago.

regression-models

4.45 score 14 scripts

mrshoenel

mmb:Arbitrary Dependency Mixed Multivariate Bayesian Models

Supports Bayesian models with full and partial (hence arbitrary) dependencies between random variables. Discrete and continuous variables are supported, and conditional joint probabilities and probability densities are estimated using Kernel Density Estimation (KDE). The full general form, which implements an extension to Bayes' theorem, as well as the simple form, which is just a Bayesian network, both support regression through segmentation and KDE and estimation of probability or relative likelihood of discrete or continuous target random variables. This package also provides true statistical distance measures based on Bayesian models. Furthermore, these measures can be facilitated on neighborhood searches, and to estimate the similarity and distance between data points. Related work is by Bayes (1763) <doi:10.1098/rstl.1763.0053> and by Scutari (2010) <doi:10.18637/jss.v035.i03>.

Maintained by Sebastian Hönel. Last updated 4 years ago.

bayes-classifier kernel-density-estimation neighborhood-search regression-models

3.70 score 5 scripts

dsy109

HoRM:Supplemental Functions and Datasets for "Handbook of Regression Methods"

Supplement for the book "Handbook of Regression Methods" by D. S. Young. Some datasets used in the book are included and documented. Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.

Maintained by Derek S. Young. Last updated 10 months ago.

regression-analysis regression-models shiny-apps

3.56 score 73 scripts

shixiangwang

regport:Regression Model Processing Port

Provides R6 classes, methods and utilities to construct, analyze, summarize, and visualize regression models.

Maintained by Shixiang Wang. Last updated 1 months ago.

batch-processing regression-models

6 stars 3.48 score 4 scripts

diogoferrari

ceser:Cluster Estimated Standard Errors

Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.

Maintained by Diogo Ferrari. Last updated 3 years ago.

clustered-standard-errors regression-models

2 stars 3.00 score 5 scripts

friendly

VisCollin:Visualizing Collinearity Diagnostics

Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009), "Where’s Waldo: Visualizing Collinearity Diagnostics", <doi:10.1198/tast.2009.0012>. These include better tabular presentation of collinearity diagnostics that highlight the important numbers, a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient, and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent.

Maintained by Michael Friendly. Last updated 1 years ago.

biplots collinearity-diagnostics graphics regression-models

1 stars 2.78 score 12 scripts